Abstract
Three nature-inspired algorithms are applied to solve Travelling Salesman Problem (TSP). The first originally developed Multi-agent Evolutionary Algorithm (MAEA) is based on multi-agent interpretation of TSP problem. An agent is assigned to a single city and builds locally its neighbourhood — a subset of cities, which are considered as local candidates to a global solution of TSP. Creating cycles — global solutions of TSP is based on Ant Colonies (AC) paradigm. Found cycles are placed in Global Table and are evaluated by genetic algorithm (GA) to modify a rank of cities in local neighbourhood. MAEA is compared with two another algorithms: artificial immune — based system (AIS) and a standard AC — both applied to TSP. We present experimental results showing that MAEA outperforms both AIS and AC algorithms.
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Skaruz, J., Seredyński, F., Gamus, M. (2005). Nature-Inspired Algorithms for the TSP. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_33
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DOI: https://doi.org/10.1007/3-540-32392-9_33
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